
We propose a frame interpolation network for LiDAR point cloud, which is conducted by exploring range images as an intermediate representation combined with CNNs.

To fully exploit spatial-temporal feature of point clouds as auxiliary information to synthesize the in-between frame, we introduce a range-adaptive feature extractor to produce spatial feature representations. On the other hand, we present a flow estimator for range images to generate high-efficient temporal feature.

We demonstrate that the proposed method can deliver a favorable performance against state-of-the-art video frame interpolation models (i.e., DAIN [1]}, Super SloMo [2]}).

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